Algorithmic Learning in a Random World

Vovk, Vladimir, Gammerman, Alex, Shafer, Glenn

ISBN 10: 1441934715 / ISBN 13: 9781441934710

Edité par Springer, 2010

A propos de cet article

Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

N° de réf. du vendeur 00015581020

Détails bibliographiques

• Titre: Algorithmic Learning in a Random World
• Éditeur: Springer
• Date de parution: 2010
• Langue: anglais
• ISBN à 10 chiffres: 1441934715
• ISBN à 13 chiffres: 9781441934710
• Reliure: Couverture souple
• État de l'article: Very Good

A propos de ce titre

Synopsis

Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.